Adapting and Enhancing Evolutionary Art for Casual Creation

2020 ◽  
pp. 17-34 ◽  
Author(s):  
Simon Colton ◽  
Jon McCormack ◽  
Sebastian Berns ◽  
Elena Petrovskaya ◽  
Michael Cook
Keyword(s):  
Evolutionary Art

Beyond Interactive Evolution: Expressing Intentions Through Fitness Functions

Leonardo ◽  
2016 ◽  
Vol 49 (3) ◽  
pp. 251-256 ◽  
Author(s):  
Penousal Machado ◽  
Tiago Martins ◽  
Hugo Amaro ◽  
Pedro H. Abreu
Keyword(s):  
Fitness Function ◽  
Interactive Art ◽  
Support Tool ◽  
Generative Art ◽  
Fitness Functions ◽  
Meta Level ◽  
Evolutionary Art ◽  
Creativity Support ◽  
The Creation ◽  
The Impact

Photogrowth is a creativity support tool for the creation of nonphotorealistic renderings of images. The authors discuss its evolution from a generative art application to an interactive evolutionary art tool and finally into a meta-level interactive art system in which users express their artistic intentions through the design of a fitness function. The authors explore the impact of these changes on the sense of authorship, highlighting the range of imagery that can be produced by the system.

scholarly journals Pixel-Based Approach for Generating Original and Imitating Evolutionary Art

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1311
Author(s):  
Yuchen Wang ◽  
Rong Xie
Keyword(s):  
Fitness Function ◽  
Visual Complexity ◽  
Generation Process ◽  
Single Object ◽  
Von Neumann ◽  
Modified Particle Swarm Optimization ◽  
Fitness Functions ◽  
Evolutionary Art ◽  
Artistic Styles ◽  
The Aesthetic

We proposed a pixel-based evolution method to automatically generate evolutionary art. Our method can generate diverse artworks, including original artworks and imitating artworks, with different artistic styles and high visual complexity. The generation process is fully automated. In order to adapt to the pixel-based method, a von Neumann neighbor topology-modified particle swarm optimization (PSO) is employed to the proposed method. The fitness functions of PSO are well prepared. Firstly, we come up with a set of aesthetic fitness functions. Next, the imitating fitness function is designed. Finally, the aesthetic fitness functions and the imitating fitness function are weighted into one single object function, which is used in the modified PSO. Both the original outputs and imitating outputs are shown. A questionnaire is designed to investigate the subjective aesthetic feeling of proposed evolutionary art, and the statistics are shown.

scholarly journals Avoiding the Inherent Limitations in Datasets Used for Measuring Aesthetics When Using a Machine Learning Approach

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Adrian Carballal ◽  
Carlos Fernandez-Lozano ◽  
Nereida Rodriguez-Fernandez ◽  
Luz Castro ◽  
Antonino Santos
Keyword(s):  
State Of The Art ◽  
Learning Approach ◽  
Aesthetic Value ◽  
Experimental Conditions ◽  
Generative Art ◽  
Machine Learning Approach ◽  
Evolutionary Art ◽  
The Aesthetic ◽  
Psychological Experiments

An important topic in evolutionary art is the development of systems that can mimic the aesthetics decisions made by human begins, e.g., fitness evaluations made by humans using interactive evolution in generative art. This paper focuses on the analysis of several datasets used for aesthetic prediction based on ratings from photography websites and psychological experiments. Since these datasets present problems, we proposed a new dataset that is a subset of DPChallenge.com. Subsequently, three different evaluation methods were considered, one derived from the ratings available at DPChallenge.com and two obtained under experimental conditions related to the aesthetics and quality of images. We observed different criteria in the DPChallenge.com ratings, which had more to do with the photographic quality than with the aesthetic value. Finally, we explored learning systems other than state-of-the-art ones, in order to predict these three values. The obtained results were similar to those using state-of-the-art procedures.

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